Met Police use electrical 'hum' to solve crimes

The Metropolitan Police is using the "hum" of background noise
produced by mains electricity to help solve crimes, it has been disclosed.

Officers in the force have compiled a database of the noise, which varies continually according to the amount of demand on the electricity network, to help verify whether audio or video evidence is genuine.

Although it cannot normally be heard without specialist equipment, the noise is analysed by the National Grid to help them work out current electricity demand.

But by comparing evidence from an investigation with the database, officers are able to work out the exact date and time someone was speaking in a recording.

The "digital forensics" team use computer programmes to strip out the background noise from the rest of the audio clip and then compare the unique "fingerprint" of the noise with their database, which records the hum continually.

Dr Alan Cooper, the service's senior digital forensic practitioner, said he was able to use the database to establish the authenticity of covert recordings made by victims of a crime in which the perpetrator has apparently admitted his guilt.

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"The fluctuation occurs because the frequency drifts in sympathy with the supply and demand," he told BBC Radio 4's Today programme. "It builds up a unique fingerprint. From a statistical standpoint, it is very, very strong evidence.

"For example, a rape victim may have a covert recording: she has gone to the perpetrator, had a conversation and tried to get him to admit to the offence. At some stage the police may want to establish that the recording is genuine.

"If we can extract the background hum from the recording, we can exactly date and time when it was made and also establish whether there has been anything added or taken away from the recording."

The analysis does not always work, but Dr Cooper claims he has been successful in the "majority of instances" when the technique has been used.

"The future is bright," he said. "The more time we spend evolving the algorithms to dig deeper into the recordings, the more of the hum we will get out."